



'''simple docstring'''

import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset

from ..utils.generic import ModelOutput

class 							a__( snake_case__     ):






			def __init__(     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase						)							->							Any:
					snake_case__	     =dataset
					snake_case__	     =process
					snake_case__	     =params






			def __len__(     self						)							->							Optional[int]:
					return len(self.dataset						)






			def __getitem__(     self					,   _UpperCAmelCase						)							->							str:
					snake_case__	     =self.dataset[i]
					snake_case__	     =self.process(_UpperCAmelCase					,   **self.params						)
					return processed





class 							a__( snake_case__     ):






			def __init__(     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase=None						)							->							str:
					snake_case__	     =loader
					snake_case__	     =infer
					snake_case__	     =params
					if loader_batch_size == 1:
							# Let's spare some time by deactivating altogether
							snake_case__	     =None
					snake_case__	     =loader_batch_size

					# Internal bookkeeping
					snake_case__	     =None
					snake_case__	     =None






			def __len__(     self						)							->							List[str]:
					return len(self.loader						)






			def __iter__(     self						)							->							Any:
					snake_case__	     =iter(self.loader						)
					return self






			def 						_lowercase  (     self						)							->							List[Any]:
					if isinstance(self._loader_batch_data					,   torch.Tensor						):
							# Batch data is simple tensor, just fetch the slice
							snake_case__	     =self._loader_batch_data[self._loader_batch_index]
					else:
							# Batch data is assumed to be BaseModelOutput (or dict)
							snake_case__	     ={}
							for k, element in self._loader_batch_data.items():
									if isinstance(_UpperCAmelCase					,   _UpperCAmelCase						):
											# Convert ModelOutput to tuple first
											snake_case__	     =element.to_tuple()
											if isinstance(element[0]					,   torch.Tensor						):
													snake_case__	     =tuple(el[self._loader_batch_index].unsqueeze(0						) for el in element						)
											elif isinstance(element[0]					,   np.ndarray						):
													snake_case__	     =tuple(np.expand_dims(el[self._loader_batch_index]					,   0						) for el in element						)
											continue
									if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(_UpperCAmelCase					,   _UpperCAmelCase						):
											# Those are stored as lists of tensors so need specific unbatching.
											if isinstance(element[0]					,   torch.Tensor						):
													snake_case__	     =tuple(el[self._loader_batch_index].unsqueeze(0						) for el in element						)
											elif isinstance(element[0]					,   np.ndarray						):
													snake_case__	     =tuple(np.expand_dims(el[self._loader_batch_index]					,   0						) for el in element						)
											continue
									if element is None:
											# This can happen for optional data that get passed around
											snake_case__	     =None
									elif isinstance(element[self._loader_batch_index]					,   torch.Tensor						):
											# Take correct batch data, but make it looked like batch_size=1
											# For compatibility with other methods within transformers

											snake_case__	     =element[self._loader_batch_index].unsqueeze(0						)
									elif isinstance(element[self._loader_batch_index]					,   np.ndarray						):
											# Take correct batch data, but make it looked like batch_size=1
											# For compatibility with other methods within transformers
											snake_case__	     =np.expand_dims(element[self._loader_batch_index]					,   0						)
									else:
											# This is typically a list, so no need to `unsqueeze`.
											snake_case__	     =element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
							snake_case__	     =self._loader_batch_data.__class__(_UpperCAmelCase						)
					self._loader_batch_index += 1
					return result






			def 						_lowercase  (     self						)							->							Union[str, Any]:
					if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
							# We are currently unrolling a batch so we just need to return
							# the current item within a batch
							return self.loader_batch_item()

					# We're out of items within a batch
					snake_case__	     =next(self.iterator						)
					snake_case__	     =self.infer(_UpperCAmelCase					,   **self.params						)
					# We now have a batch of "inferred things".
					if self.loader_batch_size is not None:
							# Try to infer the size of the batch
							if isinstance(_UpperCAmelCase					,   torch.Tensor						):
									snake_case__	     =processed
							else:
									snake_case__	     =list(processed.keys()						)[0]
									snake_case__	     =processed[key]
							if isinstance(_UpperCAmelCase					,   _UpperCAmelCase						):
									snake_case__	     =len(_UpperCAmelCase						)
							else:
									snake_case__	     =first_tensor.shape[0]
							if 0 < observed_batch_size < self.loader_batch_size:
									# could be last batch so we can't unroll as many
									# elements.
									snake_case__	     =observed_batch_size
							# Setting internal index to unwrap the batch
							snake_case__	     =processed
							snake_case__	     =0
							return self.loader_batch_item()
					else:
							# We're not unrolling batches
							return processed





class 							a__( snake_case__     ):






			def __init__(     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase=None						)							->							Dict:
					super().__init__(_UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase						)






			def __iter__(     self						)							->							Any:
					snake_case__	     =iter(self.loader						)
					snake_case__	     =None
					return self






			def 						_lowercase  (     self						)							->							Tuple:
					if self.subiterator is None:
							snake_case__	     =self.infer(next(self.iterator						)					,   **self.params						)
					try:
							# Try to return next item
							snake_case__	     =next(self.subiterator						)
					except StopIteration:
							# When a preprocess iterator ends, we can start lookig at the next item
							# ChunkIterator will keep feeding until ALL elements of iterator
							# all have created their subiterator and have been iterating against.
							#
							# Another way to look at it, is we're basically flattening lists of lists
							# into a single list, but with generators
							snake_case__	     =self.infer(next(self.iterator						)					,   **self.params						)
							snake_case__	     =next(self.subiterator						)
					return processed





class 							a__( snake_case__     ):






			def __iter__(     self						)							->							Dict:
					snake_case__	     =iter(self.loader						)
					return self






			def 						_lowercase  (     self						)							->							Union[str, Any]:
					# Extremely similar to PipelineIterator in its unpacking mechanism
					# BUT, we have an extra required item which is the presence of `is_last`
					# That is because everything is flattened by `PipelineChunkIterator` we
					# need to keep track of how to regroup here in the original `process`
					# boundaries so that `process` and `postprocess` see the same data.

					# This iterator accumulates items (possibly while unbatching) until it
					# its a `is_last` and then just passes it on to the caller.
					snake_case__	     =False
					snake_case__	     =[]
					if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
							while self._loader_batch_index < self.loader_batch_size:
									snake_case__	     =self.loader_batch_item()
									snake_case__	     =item.pop('is_last'						)
									accumulator.append(_UpperCAmelCase						)
									if is_last:
											return accumulator

					while not is_last:
							snake_case__	     =self.infer(next(self.iterator						)					,   **self.params						)
							if self.loader_batch_size is not None:
									if isinstance(_UpperCAmelCase					,   torch.Tensor						):
											snake_case__	     =processed
									else:
											snake_case__	     =list(processed.keys()						)[0]
											snake_case__	     =processed[key]
									if isinstance(_UpperCAmelCase					,   _UpperCAmelCase						):
											snake_case__	     =len(_UpperCAmelCase						)
									else:
											snake_case__	     =first_tensor.shape[0]
									if 0 < observed_batch_size < self.loader_batch_size:
											# could be last batch so we can't unroll as many
											# elements.
											snake_case__	     =observed_batch_size
									snake_case__	     =processed
									snake_case__	     =0
									while self._loader_batch_index < self.loader_batch_size:
											snake_case__	     =self.loader_batch_item()
											snake_case__	     =item.pop('is_last'						)
											accumulator.append(_UpperCAmelCase						)
											if is_last:
													return accumulator
							else:
									snake_case__	     =processed
									snake_case__	     =item.pop('is_last'						)
									accumulator.append(_UpperCAmelCase						)
					return accumulator





class 							a__( snake_case__     ):






			def __init__(     self					,   _UpperCAmelCase					,   _UpperCAmelCase						)							->							List[str]:
					snake_case__	     =dataset
					snake_case__	     =key






			def __len__(     self						)							->							Union[str, Any]:
					return len(self.dataset						)






			def __getitem__(     self					,   _UpperCAmelCase						)							->							Any:
					return self.dataset[i][self.key]





class 							a__( snake_case__     ):






			def __init__(     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase						)							->							Optional[int]:
					snake_case__	     =dataset
					snake_case__	     =keya
					snake_case__	     =keya






			def __len__(     self						)							->							Union[str, Any]:
					return len(self.dataset						)






			def __getitem__(     self					,   _UpperCAmelCase						)							->							Optional[int]:
					return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}









'''simple docstring'''

import os

SCREAMING_SNAKE_CASE__     :						Optional[Any]	=  {'''I''': 1, '''V''': 5, '''X''': 1_0, '''L''': 5_0, '''C''': 1_0_0, '''D''': 5_0_0, '''M''': 1_0_0_0}






def 				a  (     UpperCamelCase_    :    str			)       ->	int:
		snake_case__	     =0

		snake_case__	     =0
		while index < len(UpperCamelCase_			) - 1:
				snake_case__	     =SYMBOLS[numerals[index]]
				snake_case__	     =SYMBOLS[numerals[index + 1]]
				if current_value < next_value:
						total_value -= current_value
				else:
						total_value += current_value
				index += 1
		total_value += SYMBOLS[numerals[index]]

		return total_value






def 				a  (     UpperCamelCase_    :    int			)       ->	str:
		snake_case__	     =''

		snake_case__	     =num // 1000
		numerals += m_count * "M"
		num %= 1000

		snake_case__	     =num // 100
		if c_count == 9:
				numerals += "CM"
				c_count -= 9
		elif c_count == 4:
				numerals += "CD"
				c_count -= 4
		if c_count >= 5:
				numerals += "D"
				c_count -= 5
		numerals += c_count * "C"
		num %= 100

		snake_case__	     =num // 10
		if x_count == 9:
				numerals += "XC"
				x_count -= 9
		elif x_count == 4:
				numerals += "XL"
				x_count -= 4
		if x_count >= 5:
				numerals += "L"
				x_count -= 5
		numerals += x_count * "X"
		num %= 10

		if num == 9:
				numerals += "IX"
				num -= 9
		elif num == 4:
				numerals += "IV"
				num -= 4
		if num >= 5:
				numerals += "V"
				num -= 5
		numerals += num * "I"

		return numerals






def 				a  (     UpperCamelCase_    :    str = "/p089_roman.txt"			)       ->	int:
		snake_case__	     =0

		with open(os.path.dirname(UpperCamelCase_			) + roman_numerals_filename			) as filea:
				snake_case__	     =filea.readlines()

		for line in lines:
				snake_case__	     =line.strip()
				snake_case__	     =parse_roman_numerals(UpperCamelCase_			)
				snake_case__	     =generate_roman_numerals(UpperCamelCase_			)
				savings += len(UpperCamelCase_			) - len(UpperCamelCase_			)

		return savings


if __name__ == "__main__":
			print(f"""{solution() = }""")




